A Survey of Latent Fingerprint Indexing and Segmentation Based Matching (original) (raw)

Lecture notes in networks and systems, 2020

Abstract

Over the past few years, fingerprints have been considered the most sensitive and crucial identification basis for low enforcement agencies. In crime scene and forensics, recording of latent fingerprints from uneven and noisy surface is a difficult task and conventional algorithm fails in most of the times. A robust orientation field estimation algorithm is the need of the time to recognize the poor quality latent. To overcome the limitations of conventional algorithm, various techniques have been proposed in the last decade. In this paper, a comparative study has been done of state-of-the-art techniques with their advancements and limitations. Our proposal aims at effectively minimizing the difficulties faced to separate ridges and segmentation of latent images reducing search time and computational complexity while optimizing the system retrieval performance.

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